At a DAC Accellera panel this year there was some discussion on cross-company collaboration in training. The theory is that more collaboration would mean a larger training set and therefore higher accuracy in GenAI (for example in RTL generation). But semiconductor companies are very protective of their data and reports of copyrighted… Read More
Many years ago, not long after we first launched SpyGlass, I was looking around for new areas where we could apply static verification methods and was fortunate to meet Ralph Marlett, a guy (now friend) with extensive experience in DFT. Ralph joined us and went on to build the very capable SpyGlass DFT app. So capable that SpyGlass… Read More
I am on a voyage of discovery through prompting and prompting technologies because these are the critical interfaces between what we want (or roughly imagine we want) from AI, and AI’s ability to deliver. I have seen suggestions that any deficiencies today are a detail that will soon be overcome. I’m not so sure. Yes, prompting technology… Read More
How do you control your smart TV? With a remote control of course, already quite capable since it allows voice commands to find a movie or TV show without needing all that fiddly button-based control and lookup. But there’s a range of things you can’t do that we take for granted on a tablet or phone screen. Point and click on an object,… Read More
In the business press today I still find a preference for reporting proof-of-concept accomplishments for AI applications: passing a bar exam with a top grade, finding cancerous tissue in X-rays more accurately than junior radiologists, and so on. Back in the day we knew that a proof-of-concept, however appealing, had to be followed… Read More
Early last year we talked about state space models, a recent advance over large language modeling with some appealing advantages. In this blog we introduce neurosymbolic methods, another advance in foundation technologies, here applied to automated code generation. Paul Cunningham (GM, Verification at Cadence), Raúl Camposano… Read More
When I first heard about GenAI on mobile platforms – from Arm, Qualcomm and others – I confess I was skeptical. Surely there wouldn’t be enough capacity or performance to deliver more than a proof of concept? But Arm, and I’m sure others, have been working hard to demonstrate this is more than a party trick. It doesn’t hurt that foundation… Read More
In chiplet-based design we continue the march of Moore’s Law by scaling what we can put in a semiconductor package beyond the boundaries of what we can build on a single die. This style is already gaining traction in AI applications, high performance computing, and automotive, each of which aims to scale out to highly integrated … Read More
Keeping up with the furious pace of AI innovation probably doesn’t allow a lot of time for deep analysis across many use cases. However I can’t help feeling we’re sacrificing quality and ultimately end user acceptance of AI by prioritizing new capabilities over rigorous productization. I am certain that product companies do rigorous… Read More
In engineering development, automated code generation as a pair programming assistant is high on the list of targets for GenAI applications. For hardware design obvious targets would be to autogenerate custom RTL functions or variants on standard functions, or to complete RTL snippets as an aid to human-driven code generation.… Read More
